Compare multimedia frameworks in mobile platforms

Multimedia feature is currently one of the most important features in mobile devices. Many modern mobile platforms use a centralized software stack to handle multimedia requirements that software stack is called multimedia framework. Multimedia framework belongs to the middleware layer of mobile operating system. It can be considered as a bridge that connects mobile operating system kernel, hardware drivers with UI applications. It supplies high level APIs that offers simple and easy solutions for complicated multimedia tasks to UI application developers. Multimedia Framework also manages and utilizes low lever system software and hardware in an efficient manner. It offers a centralize solution between high level demands and low level system resources. In this M.Sc. thesis project we have studied, analyzed and compared open source GStreamer, Android Stagefright and Microsoft Silverlight Media Framework from several perspectives. Some of the comparison perspectives are architecture, supported use cases, extensibility, implementation language and program language support (bindings), developer support, and legal status aspects. One of the main contributions of this thesis work is that clarifying in details the strength and weaknesses of each framework. Furthermore, the thesis should serve decision-making guidance when on needs to select a multimedia framework for a project. Moreover, and to enhance the impression with the three multimedia frameworks, a basic media player implementation is demonstrated with source code in the thesis.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format

APEX2S: A Two-Layer Machine Learning Model for Discovery of host-pathogen protein-protein Interactions on Cloud-based Multiomics Data

Presented by the avalanche of biological interactions data, computational biology is now facing greater challenges on big data analysis and solicits more studies to mine and integrate cloud-based multiomics data, especially when the data are related to infectious diseases. Meanwhile, machine learning techniques have recently succeeded in different computational biology tasks. In this article, we have calibrated the focus for host-pathogen protein-protein interactions study, aiming to apply the machine learning techniques for learning the interactions data and making predictions. A comprehensive and practical workflow to harness different cloud-based multiomics data is discussed. In particular, a novel two-layer machine learning model, namely APEX2S, is proposed for discovery of the protein-protein interactions data. The results show that our model can better learn and predict from the accumulated host-pathogen protein-protein interactions

The noise control of minicar body in white based on acoustic panel participation method

It is very important to predict the acoustic radiation of vehicle body for the control of interior noise. Firstly, the kinetic equations of coupled acoustic-structural finite element method are explained and the numerical analytical methods of noise transfer function and acoustic panel participation are further obtained. Then the coupled acoustic-structural finite element model of body in white and passenger compartment cavity of a minicar is established and verified by modal test. The passive side of engine mounting points are chosen as the excitation points, and driver’s right ear is the output point of sound pressure response. The noise transfer function is calculated and the critical frequency of vehicle interior noise is obtained. The acoustic panel participation analysis of vehicle roof and floor are conducted, and the key acoustic panels are identified. In order to reduce the noise of critical frequency, the measures, pasting damping material and welding beam, are adopted. The results indicate that, compared with the results of structure improvement of modal method, the vehicle interior noise is controlled more effectively by using the acoustic panel participation analytical method